Module 12: Factors with Forcats

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.right-column[
# Module 12:  Factors with Forcats
###

**Graham Eglit**<br>
Axsome Therapeutics<br>
Fall 2024
]

---
class: inverse, center, middle

# Factors Basics!
----
<svg viewBox="0 0 581 512" style="position:relative;display:inline-block;top:.1em;fill:white;height:3em;" xmlns="http://www.w3.org/2000/svg">  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>

---

.center[
# Factors
]

.pull-left[
- Factors are used to work with categorical variables
   - Categorical variables = variables with a fixed and known set of possible values
      - e.g., eye color, treatment group, etc. <br> <br>
- We've come across factors before back in Module 2
   - Recall that factors are integers that look like characters
   - Ideal for statistical modeling
   - We'll also use them for ordering character strings
      - e.g., for visualization
]

.pull-right[
<br>
<img src = "data:image/png;base64,#https://media.giphy.com/media/ibAtCaoRZHNFKP9te0/giphy.gif" />
.center[
.caption[
Via [Giphy](https://media.giphy.com/media/ibAtCaoRZHNFKP9te0/giphy.gif)
]
]
]

---

.center[
# Creating Factors
]

- Use the `factor` function to create factors
- Use the `levels` argument to specify levels of the factor
- Use the `labels` argument to change the labels associated with each level of a factor

```r
x <- c("Tues", "Sat", "Thur")
x
```

```
## [1] "Tues" "Sat"  "Thur"
```

```r
x <- factor(x, levels = c("Sun", "Mon", "Tues", "Wed", "Thur", "Fri", "Sat"),
            labels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))
x
```

```
## [1] Tuesday  Saturday Thursday
## Levels: Sunday Monday Tuesday Wednesday Thursday Friday Saturday
```

---
class: inverse, center, middle

# General Social Survey
----
<svg viewBox="0 0 581 512" style="position:relative;display:inline-block;top:.1em;fill:white;height:3em;" xmlns="http://www.w3.org/2000/svg">  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>

---

.center[
# The General Social Survey: `gss_cat`
]

<div id="htmlwidget-540182f28bc8744f6020" style="width:100%;height:auto;" class="datatables html-widget"></div>
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14999","$25000 or more","$25000 or more","Not applicable","$10000 - 14999","Not applicable","$25000 or more","Not applicable","$15000 - 19999","Not applicable","Not applicable","Not applicable","Not applicable","Not applicable","Not applicable","$10000 - 14999","Not applicable","Not applicable","$25000 or more","$25000 or more","$25000 or more","$3000 to 3999","Not applicable","Refused","$3000 to 3999","Not applicable","$20000 - 24999","$25000 or more","$10000 - 14999","Not applicable","Refused","$25000 or more","$25000 or more","$15000 - 19999","$20000 - 24999","$25000 or more","Refused","Lt $1000","Not applicable","Refused"],["Ind,near rep","Not str republican","Independent","Ind,near rep","Not str democrat","Strong democrat","Not str republican","Ind,near dem","Not str democrat","Strong republican","Not str democrat","Ind,near rep","Strong democrat","Strong republican","Ind,near dem","Strong democrat","Strong republican","Independent","Not str democrat","Independent","Ind,near 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democrat","Not str republican","Strong democrat","Not str republican","Strong democrat","Ind,near dem","Strong democrat","Strong democrat","Independent","Strong democrat","Ind,near dem","Not str democrat","Not str republican","Independent","Not str democrat","Ind,near dem","Not str republican","Not str republican","Independent","Ind,near dem","Not str republican","Independent"],["Protestant","Protestant","Protestant","Orthodox-christian","None","Protestant","Christian","Protestant","Protestant","Protestant","Protestant","None","Protestant","Protestant","None","Protestant","Jewish","None","Catholic","Protestant","Catholic","Protestant","None","Christian","Protestant","Protestant","Protestant","Protestant","Protestant","Protestant","Protestant","Catholic","Protestant","None","Protestant","Christian","None","Catholic","Catholic","Protestant","None","Catholic","None","Other","None","Catholic","Protestant","Protestant","Protestant","Protestant","Protestant","Protestant","Protestant","Protestant","None","None","Protestant","Protestant","Protestant","Protestant","Protestant","Christian","Protestant","Protestant","None","None","Catholic","Protestant","Protestant","Protestant","Protestant","Other","Protestant","Protestant","Protestant","Protestant","Protestant","Protestant","Catholic","Protestant","Protestant","Protestant","Catholic","Protestant","Protestant","Protestant","Protestant","Protestant","Catholic","Catholic","Catholic","Catholic","Inter-nondenominational","Catholic","Catholic","Catholic","Catholic","None","None","None"],["Southern 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---
class: inverse, center, middle

# Modifying Factor Order
----
<svg viewBox="0 0 581 512" style="position:relative;display:inline-block;top:.1em;fill:white;height:3em;" xmlns="http://www.w3.org/2000/svg">  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>

---

.center[
# fct_reorder
]

.panelset[

.panel[.panel-name[Overview]

- all `forcats` functions begin with `fct_*`
  - easy to use with function completion <br> <br>
- `fct_reorder` re-orders levels of a factor
    - arguments
       - `f` = factors whose levels you want to modify
       - `x` = a numeric vector that you want to use to reorder the levels
       - `fun` = optional, a function that's used if there are multiple values of `x` for each value of `f`

]

.panel[.panel-name[Original Visualization]

.pull-left[

```r
relig_summary <- gss_cat %>%
  group_by(relig) %>%
  summarise(
    age = mean(age, na.rm = TRUE),
    tvhours = mean(tvhours, na.rm = TRUE),
    n = n()
  )

ggplot(relig_summary, aes(tvhours, relig)) + geom_point()
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-5-1.png)

]
]

.panel[.panel-name[Re-Ordered Visualization]

.pull-left[

```r
relig_summary %>%
  mutate(relig = fct_reorder(relig, tvhours)) %>%
  ggplot(aes(tvhours, relig)) +
    geom_point()
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-7-1.png)

]
]

]

---

.center[
# How Could we Improve This Plot?
]

.panelset[

.panel[.panel-name[Ideas?]

.center[
![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-8-1.png)
]

]

.panel[.panel-name[Option 2]

.pull-left[

```r
relig_summary %>%
  mutate(relig = fct_reorder(relig, tvhours)) %>%
  ggplot(aes(tvhours, relig)) +
  geom_col(aes(fill = relig)) +
  geom_text(aes(label = round(tvhours, 2)), hjust = 1.2, color = "white", fontface = 2) + 
  theme_light() + 
  labs(x = "Hours of TV Consumption", y = NULL, fill = "Religious Affiliation")
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-10-1.png)

]
]

.panel[.panel-name[Option 2]

.pull-left[

```r
relig_summary %>%
  mutate(relig = fct_reorder(relig, tvhours)) %>%
  ggplot(aes(x = tvhours, y = relig)) +
  geom_segment(aes(x = 0, xend = tvhours, y = relig, yend = relig)) + 
  geom_point() + 
  scale_x_continuous(expand = c(0, 0, 0, .5)) + 
  labs(x = "Hours of TV Consumption", y = NULL, fill = "Religious Affiliation") + 
  theme_light() 
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-12-1.png)

]
]

.panel[.panel-name[Option 3]

.pull-left[

```r
relig_summary %>%
  mutate(relig = fct_reorder(relig, tvhours)) %>%
  ggplot(aes(x = tvhours, y = relig, label = round(tvhours, 2))) +
  geom_segment(aes(x = 0, xend = tvhours, y = relig, yend = relig)) + 
  geom_label() + 
  scale_x_continuous(expand = c(0, 0, 0, .5)) + 
  labs(x = "Hours of TV Consumption", y = NULL, fill = "Religious Affiliation") + 
  theme_light() 
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-14-1.png)

]
]

]

---

.center[
# fct_relevel
]

.panelset[

.panel[.panel-name[Overview]

- `fct_relevel` re-levels a factor
    - arguments
       - `f` = factors whose levels you want to modify
       - `...` = any number of levels that you want to move to the front of the line <br> <br>
- `fct_reorder` changes the order of factor levels using a numeric vector or an arithmetic function (e.g., mean)
- `fct_relvel` changes to order of factor levels through manual re-specification

]

.panel[.panel-name[Original Visualization]

.pull-left[

```r
rincome_summary <- gss_cat %>%
  group_by(rincome) %>%
  summarise(
    age = mean(age, na.rm = TRUE),
    tvhours = mean(tvhours, na.rm = TRUE),
    n = n())

ggplot(rincome_summary, aes(age, rincome)) + geom_point()
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-16-1.png)

]
]

.panel[.panel-name[Re-Ordered Visualization]

.pull-left[

```r
ggplot(rincome_summary, aes(age, fct_relevel(rincome, "Not applicable"))) +
  geom_point()
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-18-1.png)

]
]

]

---

.center[
# fct_reorder2
]

- `fct_reorder2` reorders the factor by the y values associated with the largest x values

.panelset[

.panel[.panel-name[Create Data]

.pull-left[

```r
by_age <- gss_cat %>%
  filter(!is.na(age)) %>%
  count(age, marital) %>%
  group_by(age) %>%
  mutate(prop = n / sum(n))
```

]

.pull-right[

```r
by_age %>% head(n = 5)
```

```
## # A tibble: 5 × 4
## # Groups:   age [2]
##     age marital           n    prop
##   <int> <fct>         <int>   <dbl>
## 1    18 Never married    89 0.978  
## 2    18 Married           2 0.0220 
## 3    19 Never married   234 0.940  
## 4    19 Divorced          3 0.0120 
## 5    19 Widowed           1 0.00402
```

]

]

.panel[.panel-name[Figures]

.pull-left[

**Original**

```r
ggplot(by_age, aes(age, prop, colour = marital)) +
  geom_line(na.rm = TRUE)
```

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-21-1.png)

]

.pull-right[

**Re-Ordered**

```r
ggplot(by_age, aes(age, prop, colour = fct_reorder2(marital, age, prop))) +
  geom_line() + labs(color = "marital")
```

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-22-1.png)

]
]

]

---

.center[
# fct_infreq
]

- `fct_infreq()` orders factor levels by increasing frequency
- Useful in bar plots
- Combine with `fct_rev()` to order by decreasing frequency

.panelset[

.panel[.panel-name[fct_infreq]

.pull-left[

```r
gss_cat %>%
  mutate(marital = fct_infreq(marital)) %>%
  ggplot(aes(marital)) +
    geom_bar()
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-24-1.png)

]

]

.panel[.panel-name[combine w/ fct_rev]

.pull-left[

```r
gss_cat %>%
  mutate(marital = fct_rev(fct_infreq(marital))) %>%
  ggplot(aes(marital)) +
    geom_bar()
```

]

.pull-right[

![](data:image/png;base64,#mod12_files/figure-html/unnamed-chunk-26-1.png)

]
]

]

---
class: inverse, center, middle

# Modifying Factor Levels
----
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---
.center[
# Modifying Factor Levels
]

- Previous `fct_*` functions allowed you to re-order factor levels
- These next functions will enable you to change the values of factor levels
   - `fct_recode`: allows you to change the value of each level
   - `fct_collapse`: allows you to combine factor levels
   - `fct_lump`: allows you to lump together small groups
   - `fct_drop`: allows you to remove factor levels that do not have any members
   
   
---

.center[
# fct_recode
]

- `fct_recode`: allows you to change the value of each level
   - "new name" = "old name"

.panelset[

.panel[.panel-name[Original Data]

.pull-left[

```r
gss_cat %>% count(partyid) %>% head(n = 7)
```

]

.pull-right[

|partyid            |    n|
|:------------------|----:|
|No answer          |  154|
|Don't know         |    1|
|Other party        |  393|
|Strong republican  | 2314|
|Not str republican | 3032|
|Ind,near rep       | 1791|
|Independent        | 4119|

]

]

.panel[.panel-name[Recode]

.pull-left[

```r
gss_cat %>%
  mutate(partyid = fct_recode(partyid,
    "Republican, strong"    = "Strong republican",
    "Republican, weak"      = "Not str republican",
    "Independent, near rep" = "Ind,near rep",
    "Independent, near dem" = "Ind,near dem",
    "Democrat, weak"        = "Not str democrat",
    "Democrat, strong"      = "Strong democrat"
  )) %>% count(partyid) %>% head(n = 7)
```

]

.pull-right[

|partyid               |    n|
|:---------------------|----:|
|No answer             |  154|
|Don't know            |    1|
|Other party           |  393|
|Republican, strong    | 2314|
|Republican, weak      | 3032|
|Independent, near rep | 1791|
|Independent           | 4119|

]
]

]

---

.center[
# Combine Levels with fct_recode
]

- To combine groups, you can assign multiple old levels to the same new level

.pull-left[

]

.pull-right[

|partyid               |    n|
|:---------------------|----:|
|Other                 |  548|
|Republican, strong    | 2314|
|Republican, weak      | 3032|
|Independent, near rep | 1791|
|Independent           | 4119|
|Independent, near dem | 2499|
|Democrat, weak        | 3690|
|Democrat, strong      | 3490|
]

---

.center[
# fct_collapse
]

- `fct_collapse`: allows you to combine factor levels
   - useful when you want to collapse a lot levels
   - "new level" = c("old level 1", "old level 2", "old level 3")

.pull-left[

```r
gss_cat %>%
  mutate(partyid = fct_collapse(partyid,
    other = c("No answer", "Don't know", "Other party"),
    rep = c("Strong republican", "Not str republican"),
    ind = c("Ind,near rep", "Independent", "Ind,near dem"),
    dem = c("Not str democrat", "Strong democrat")
  )) %>%
  count(partyid)
```

]

.pull-right[

|partyid |    n|
|:-------|----:|
|other   |  548|
|rep     | 5346|
|ind     | 8409|
|dem     | 7180|

]

---

.center[
# fct_lump
]

- `fct_lump` combines smallest levels into an "Other" level group
   - default behavior is to lump together the smallest groups ensuring that the aggregate group is still the smallest group
   - you can use the `n` parameter to specify how many groups (excluding other) you want to keep
   
   
   
   
   
.panelset[

.panel[.panel-name[Default]

.pull-left[

```r
gss_cat %>%
  mutate(relig = fct_lump(relig)) %>%
  count(relig)
```

]

.pull-right[

|relig      |     n|
|:----------|-----:|
|Protestant | 10846|
|Other      | 10637|

]
]

.panel[.panel-name[Use n]

.pull-left[

```r
gss_cat %>%
  mutate(relig = fct_lump(relig, n = 4)) %>%
  count(relig, sort = TRUE) %>%
```

]

.pull-right[

|relig      |     n|
|:----------|-----:|
|Protestant | 10846|
|Catholic   |  5124|
|None       |  3523|
|Other      |  1301|
|Christian  |   689|

]
]

]

---

.center[
# fct_drop
]

- `fct_drop`: allows you to remove factor levels that do not have any members

```r
f <- factor(c("a", "b"), levels = c("a", "b", "c"))
f
```

```
## [1] a b
## Levels: a b c
```

```r
fct_drop(f)
```

```
## [1] a b
## Levels: a b
```

---

.center[
# Now You Try!
]

- Change the factor levels of the `gss_cat` variable `marital` to the following three categories:
   - No Answer
   - Not Married - Combination of Never Married, Separated, Divorced, and Widowed
   - Married
- Print out the frequency count of these three categories

- Now, perform the same task using a different `fct_*` function

---

.center[
# Solution
]

**Solution 1**

```r
gss_cat %>%
  mutate(marital = fct_recode(marital, 
                              "Unmarried" = "Never married",
                              "Unmarried" = "Separated",
                              "Unmarried" = "Divorced",
                              "Unmarried" = "Widowed")) %>% 
  count(marital)
```

**Solution 2**

```r
gss_cat %>%
  mutate(marital = fct_collapse(marital,
                                "Unmarried" = c("Never married", "Separated", "Divorced", "Widowed"))) %>% 
  count(marital)
```

---
class: inverse, center, middle

# Recap!
----
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---

.center[
# Recap!
]

.pull-left[
**Modifying Factor Order**
- `fct_reorder` <br> <br> 
- `fct_relevel` <br> <br> 
- `fct_reorder2` <br> <br> 
- `fct_infreq`
   - `fct_rev` <br> <br> 
]

.pull-right[
**Modifying Factor Levels**
- `fct_recode` <br> <br> 
- `fct_collapse` <br> <br> 
- `fct_lump` <br> <br> 
- `fct_drop` <br> <br> 
]